scheduleTaskList: Minimize Expression Start Time

In makeParallel: Transform Serial R Code into Parallel R Code

Description

Implementation of "list scheduling". This is a greedy algorithm that assigns each expression to the earliest possible processor.

Usage

scheduleTaskList(graph, maxWorker = 2L, exprTime = NULL,
  exprTimeDefault = 1e-05, sizeDefault = as.numeric(utils::object.size(1L)),
  overhead = 8e-06, bandwidth = 1.5e+09)

Arguments

graph	object of class DependGraph as returned from inferGraph
maxWorker	integer maximum number of processors
exprTime	time in seconds to execute each expression
exprTimeDefault	numeric time in seconds to execute a single expression. This will only be used if exprTime is NULL.
sizeDefault	numeric default size of objects to transfer in bytes
overhead	numeric seconds to send any object
bandwidth	numeric speed that the network can transfer an object between processors in bytes per second. We don't take network contention into account. This will have to be extended to account for multiple machines.

Details

This function is experimental and unstable. If you're trying to actually speed up your code through parallelism then consider using the default method in schedule for data parallelism. This function rewrites code to use task parallelism. Task parallelism means two or more processors run different R expressions simultaneously.

Value

schedule object of class TaskSchedule

References

Algorithm 10 in Task Scheduling for Parallel Systems, Sinnen (2007)

Examples

code <- parse(text = "a <- 100
     b <- 200
     c <- a + b")

g <- inferGraph(code)
s <- scheduleTaskList(g)
plot(s)

makeParallel documentation built on May 2, 2019, 9:40 a.m.